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Ajouter notes et légendes aux images
7 février 2011, parPour pouvoir ajouter notes et légendes aux images, la première étape est d’installer le plugin "Légendes".
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Modification lors de l’ajout d’un média
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Support de tous types de médias
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Participer à sa documentation
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FFMPEG Concat only showing first video
7 août 2017, par user5839I ;m struggling with concat
My command (via Process Builder)
[ffmpeg, -y, -i,
C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-i, C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-i, C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-i, C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-i, C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-i, C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-i, C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-i, C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-i, C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-i, C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-i, C :\workspace\processor.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4,
-filter_complex, [0:v:0][0:a:0][1:v:0][1:a:0][2:v:0][2:a:0][3:v:0][3:a:0][4:v:0][4:a:0][5:v:0][5:a:0][6:v:0][6:a:0][7:v:0][7:a:0][8:v:0][8:a:0][9:v:0][9:a:0][10:v:0][10:a:0]concat=n=11:v=1:a=1
[v][a], -map, [v], -map, [a],
C :\workspace\processor.\temp_dir\8d66984a-0b0e-4956-95d6-66eddf14b47e.mp4]And the above error repeats over and over again. The video completes and it is the right size and duration, it just plays the first video and then stops.
Any clues !?
EDIT With latest version
ffmpeg version 3.3.2 Copyright (c) 2000-2017 the FFmpeg developers
built with gcc 7.1.0 (GCC)
configuration: --enable-gpl --enable-version3 --enable-cuda --enable-cuvid --enable-d3d11va --enable-dxva2 --enable-libmfx --enable-nvenc --enable-avisynth --enable-bzlib --enable-fontconfig --enable-frei0r --enable-gnutls --enable-iconv --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libfreetype --enable-libgme --enable-libgsm --enable-libilbc --enable-libmodplug --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopus --enable-librtmp --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs --enable-libxvid --enable-libzimg --enable-lzma --enable-zlib
libavutil 55. 58.100 / 55. 58.100
libavcodec 57. 89.100 / 57. 89.100
libavformat 57. 71.100 / 57. 71.100
libavdevice 57. 6.100 / 57. 6.100
libavfilter 6. 82.100 / 6. 82.100
libswscale 4. 6.100 / 4. 6.100
libswresample 2. 7.100 / 2. 7.100
libpostproc 54. 5.100 / 54. 5.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #0:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #1, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #1:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #1:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #2, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #2:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #2:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #3, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #3:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #3:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #4, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #4:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #4:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #5, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #5:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #5:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #6, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #6:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #6:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #7, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #7:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #7:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #8, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #8:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #8:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #9, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #9:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #9:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #10, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\workspace\processor\.\temp_dir\8fdcc1c9-6275-4ef5-b9e7-05bd5cfe51ab_3840.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf55.33.100
Duration: 00:00:06.40, start: 0.000000, bitrate: 18134 kb/s
Stream #10:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3840x1920, 17995 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #10:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 130 kb/s (default)
Metadata:
handler_name : SoundHandler
Stream mapping:
Stream #0:0 (h264) -> concat:in0:v0
Stream #0:1 (aac) -> concat:in0:a0
Stream #1:0 (h264) -> concat:in1:v0
Stream #1:1 (aac) -> concat:in1:a0
Stream #2:0 (h264) -> concat:in2:v0
Stream #2:1 (aac) -> concat:in2:a0
Stream #3:0 (h264) -> concat:in3:v0
Stream #3:1 (aac) -> concat:in3:a0
Stream #4:0 (h264) -> concat:in4:v0
Stream #4:1 (aac) -> concat:in4:a0
Stream #5:0 (h264) -> concat:in5:v0
Stream #5:1 (aac) -> concat:in5:a0
Stream #6:0 (h264) -> concat:in6:v0
Stream #6:1 (aac) -> concat:in6:a0
Stream #7:0 (h264) -> concat:in7:v0
Stream #7:1 (aac) -> concat:in7:a0
Stream #8:0 (h264) -> concat:in8:v0
Stream #8:1 (aac) -> concat:in8:a0
Stream #9:0 (h264) -> concat:in9:v0
Stream #9:1 (aac) -> concat:in9:a0
Stream #10:0 (h264) -> concat:in10:v0
Stream #10:1 (aac) -> concat:in10:a0
concat:out:v0 -> Stream #0:0 (libx264)
concat:out:a0 -> Stream #0:1 (aac)
Press [q] to stop, [?] for help
[libx264 @ 000000000287d1c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 000000000287d1c0] profile High, level 5.1
Output #0, mp4, to 'C:\workspace\processor\.\temp_dir\30965932-c67a-4948-984c-613b2f06ce04.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf57.71.100
Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p(progressive), 3840x1920, q=-1--1, 25 fps, 12800 tbn, 25 tbc (default)
Metadata:
encoder : Lavc57.89.100 libx264
Side data:
cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
Stream #0:1: Audio: aac (LC) ([64][0][0][0] / 0x0040), 48000 Hz, stereo, fltp, 128 kb/s (default)
Metadata:
encoder : Lavc57.89.100 aac
video:148240kB audio:1120kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.035871%
[libx264 @ 000000000287d1c0] mb I I16..4: 31.1% 40.5% 28.4%
[libx264 @ 000000000287d1c0] mb P I16..4: 12.2% 19.8% 4.0% P16..4: 35.5% 9.9% 2.8% 0.0% 0.0% skip:15.6%
[libx264 @ 000000000287d1c0] mb B I16..4: 2.4% 2.6% 0.6% B16..8: 42.0% 4.6% 0.4% direct: 1.4% skip:45.9% L0:46.3% L1:49.4% BI: 4.3%
[libx264 @ 000000000287d1c0] 8x8 transform intra:53.4% inter:73.3%
[libx264 @ 000000000287d1c0] coded y,uvDC,uvAC intra: 30.9% 31.9% 4.6% inter: 12.0% 14.0% 0.1%
[libx264 @ 000000000287d1c0] i16 v,h,dc,p: 25% 41% 11% 23%
[libx264 @ 000000000287d1c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 26% 31% 27% 2% 2% 2% 4% 2% 3%
[libx264 @ 000000000287d1c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 33% 32% 14% 2% 4% 4% 5% 3% 3%
[libx264 @ 000000000287d1c0] i8c dc,h,v,p: 54% 23% 18% 4%
[libx264 @ 000000000287d1c0] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 000000000287d1c0] ref P L0: 65.3% 17.7% 13.9% 3.1%
[libx264 @ 000000000287d1c0] ref B L0: 90.7% 9.0% 0.3%
[libx264 @ 000000000287d1c0] ref B L1: 99.6% 0.4%
[libx264 @ 000000000287d1c0] kb/s:17249.64
[aac @ 000000000287b000] Qavg: 1577.720 -
Benefits and Shortcomings of Multi-Touch Attribution
13 mars 2023, par Erin — Analytics TipsFew sales happen instantly. Consumers take their time to discover, evaluate and become convinced to go with your offer.
Multi-channel attribution (also known as multi-touch attribution or MTA) helps businesses better understand which marketing tactics impact consumers’ decisions at different stages of their buying journey. Then double down on what’s working to secure more sales.
Unlike standard analytics, multi-channel modelling combines data from various channels to determine their cumulative and independent impact on your conversion rates.
The main benefit of multi-touch attribution is obvious : See top-performing channels, as well as those involved in assisted conversions. The drawback of multi-touch attribution : It comes with a more complex setup process.
If you’re on the fence about getting started with multi-touch attribution, here’s a summary of the main arguments for and against it.
What Are the Benefits of Multi-Touch Attribution ?
Remember an old parable of blind men and an elephant ?
Each one touched the elephant and drew conclusions about how it might look. The group ended up with different perceptions of the animal and thought the others were lying…until they decided to work together on establishing the truth.
Multi-channel analytics works in a similar way : It reconciles data from various channels and campaign types into one complete picture. So that you can get aligned on the efficacy of different campaign types and gain some other benefits too.
Better Understanding of Customer Journeys
On average, it takes 8 interactions with a prospect to generate a conversion. These interactions happen in three stages :
- Awareness : You need to introduce your company to the target buyers and pique their interest in your solution (top-of-the-funnel).
- Consideration : The next step is to channel this casual interest into deliberate research and evaluation of your offer (middle-of-the-funnel).
- Decision : Finally, you need to get the buyer to commit to your offer and close the deal (bottom-of-the-funnel).
You can analyse funnels using various attribution models — last-click, fist-click, position-based attribution, etc. Each model, however, will spotlight the different element(s) of your sales funnel.
For example, a single-touch attribution model like last-click zooms in on the bottom-of-the-funnel stage. You can evaluate which channels (or on-site elements) sealed the deal for the prospect. For example, a site visitor arrived from an affiliate link and started a free trial. In this case, the affiliate (referral traffic) gets 100% credit for the conversion.
This measurement tactic, however, doesn’t show which channels brought the customer to the very bottom of your funnel. For instance, they may have interacted with a social media post, your landing pages or a banner ad before that.
Multi-touch attribution modelling takes funnel analysis a notch further. In this case, you map more steps in the customer journey — actions, events, and pages that triggered a visitor’s decision to convert — in your website analytics tool.
Then, select a multi-touch attribution model, which provides more backward visibility aka allows you to track more than one channel, preceding the conversion.
For example, a Position Based attribution model reports back on all interactions a site visitor had between their first visit and conversion.
A prospect first lands at your website via search results (Search traffic), which gets a 40% credit in this model. Two days later, the same person discovers a mention of your website on another blog and visits again (Referral traffic). This time, they save the page as a bookmark and revisit it again in two more days (Direct traffic). Each of these channels will get a 10% credit. A week later, the prospect lands again on your site via Twitter (Social) and makes a request for a demo. Social would then receive a 40% credit for this conversion. Last-click would have only credited social media and first-click — search engines.
The bottom line : Multi-channel attribution models show how different channels (and marketing tactics) contribute to conversions at different stages of the customer journey. Without it, you get an incomplete picture.
Improved Budget Allocation
Understanding causal relationships between marketing activities and conversion rates can help you optimise your budgets.
First-click/last-click attribution models emphasise the role of one channel. This can prompt you toward the wrong conclusions.
For instance, your Facebook ads campaigns do great according to a first-touch model. So you decide to increase the budget. What you might be missing though is that you could have an even higher conversion rate and revenue if you fix “funnel leaks” — address high drop-off rates during checkout, improve page layout and address other possible reasons for exiting the page.
Funnel reports at Matomo allow you to see how many people proceed to the next conversion stage and investigate why they drop off. By knowing when and why people abandon their purchase journey, you can improve your marketing velocity (aka the speed of seeing the campaign results) and your marketing costs (aka the budgets you allocate toward different assets, touchpoints and campaign types).
Or as one of the godfathers of marketing technology, Dan McGaw, explained in a webinar :
“Once you have a multi-touch attribution model, you [can] actually know the return on ad spend on a per-campaign basis. Sometimes, you can get it down to keywords. Sometimes, you can get down to all kinds of other information, but you start to realise, “Oh, this campaign sucks. I should shut this off.” And then really, that’s what it’s about. It’s seeing those campaigns that suck and turning them off and then taking that budget and putting it into the campaigns that are working”.
More Accurate Measurements
The big boon of multi-channel marketing attribution is that you can zoom in on various elements of your funnel and gain granular data on the asset’s performance.
In other words : You get more accurate insights into the different elements involved in customer journeys. But for accurate analytics measurements, you must configure accurate tracking.
Define your objectives first : How do you want a multi-touch attribution tool to help you ? Multi-channel attribution analysis helps you answer important questions such as :
- How many touchpoints are involved in the conversions ?
- How long does it take for a lead to convert on average ?
- When and where do different audience groups convert ?
- What is your average win rate for different types of campaigns ?
Your objectives will dictate which multi-channel modelling approach will work best for your business — as well as the data you’ll need to collect.
At the highest level, you need to collect two data points :
- Conversions : Desired actions from your prospects — a sale, a newsletter subscription, a form submission, etc. Record them as tracked Goals.
- Touchpoints : Specific interactions between your brand and targets — specific page visits, referral traffic from a particular marketing channel, etc. Record them as tracked Events.
Your attribution modelling software will then establish correlation patterns between actions (conversions) and assets (touchpoints), which triggered them.
The accuracy of these measurements, however, will depend on the quality of data and the type of attribution modelling used.
Data quality stands for your ability to procure accurate, complete and comprehensive information from various touchpoints. For instance, some data won’t be available if the user rejected a cookie consent banner (unless you’re using a privacy-focused web analytics tool like Matomo).
Different attribution modelling techniques come with inherent shortcomings too as they don’t accurately represent the average sales cycle length or track visitor-level data, which allows you to understand which customer segments convert best.
Learn more about selecting the optimal multi-channel attribution model for your business.
What Are the Limitations of Multi-Touch Attribution ?
Overall, multi-touch attribution offers a more comprehensive view of the conversion paths. However, each attribution model (except for custom ones) comes with inherent assumptions about the contribution of different channels (e.g,. 25%-25%-25%-25% in linear attribution or 40%-10%-10%-40% in position-based attribution). These conversion credit allocations may not accurately represent the realities of your industry.
Also, most attribution models don’t reflect incremental revenue you gain from existing customers, which aren’t converting through analysed channels. For example, account upgrades to a higher tier, triggered via an in-app offer. Or warranty upsell, made via a marketing email.
In addition, you should keep in mind several other limitations of multi-touch attribution software.
Limited Marketing Mix Analysis
Multi-touch attribution tools work in conjunction with your website analytics app (as they draw most data from it). Because of that, such models inherit the same visibility into your marketing mix — a combo of tactics you use to influence consumer decisions.
Multi-touch attribution tools cannot evaluate the impact of :
- Dark social channels
- Word-of-mouth
- Offline promotional events
- TV or out-of-home ad campaigns
If you want to incorporate this data into your multi-attribution reporting, you’ll have to procure extra data from other systems — CRM, ad measurement partners, etc, — and create complex custom analytics models for its evaluation.
Time-Based Constraints
Most analytics apps provide a maximum 90-day lookback window for attribution. This can be short for companies with longer sales cycles.
Source : Marketing Charts Marketing channels can be overlooked or underappreciated when your attribution window is too short. Because of that, you may curtail spending on brand awareness campaigns, which, in turn, will reduce the number of people entering the later stages of your funnel.
At the same time, many businesses would also want to track a look-forward window — the revenue you’ll get from one customer over their lifetime. In this case, not all tools may allow you to capture accurate information on repeat conversions — through re-purchases, account tier updates, add-ons, upsells, etc.
Again, to get an accurate picture you’ll need to understand how far into the future you should track conversions. Will you only record your first sales as a revenue number or monitor customer lifetime value (CLV) over 3, 6 or 12 months ?
The latter is more challenging to do. But CLV data can add another depth of dimension to your modelling accuracy. With Matomo, you set up this type of tracking by using our visitors’ tracking feature. We can help you track select visitors with known identifiers (e.g. name or email address) to discover their visiting patterns over time.
Limited Access to Raw Data
In web analytics, raw data stands for unprocessed website visitor information, stripped from any filters, segmentation or sampling applied.
Data sampling is a practice of analysing data subsets (instead of complete records) to extrapolate findings towards the entire data set. Google Analytics 4 applies data sampling once you hit over 500k sessions at the property level. So instead of accurate, real-life reporting, you receive approximations, generated by machine learning models. Data sampling is one of the main reasons behind Google Analytics’ accuracy issues.
In multi-channel attribution modelling, usage of sampled data creates further inconsistencies between the reports and the actual state of affairs. For instance, if your website generates 5 million page views, GA multi-touch analytical reports are based on the 500K sample size aka only 90% of the collected information. This hardly represents the real effect of all marketing channels and can lead to subpar decision-making.
With Matomo, the above is never an issue. We don’t apply data sampling to any websites (no matter the volume of traffic) and generate all the reports, including multi-channel attribution ones, based on 100% real user data.
AI Application
On the other hand, websites with smaller traffic volumes often have limited sampling datasets for building attribution models. Some tracking data may also be not available because the visitor rejected a cookie banner, for instance. On average, less than 50% of users in Australia, France, Germany, Denmark and the US among other countries always consent to all cookies.
To compensate for such scenarios, some multi-touch attribution solutions apply AI algorithms to “fill in the blanks”, which impacts the reporting accuracy. Once again, you get approximate data of what probably happened. However, Matomo is legally exempt from showing a cookie consent banner in most EU markets. Meaning you can collect 100% accurate data to make data-driven decisions.
Difficult Technical Implementation
Ever since attribution modelling got traction in digital marketing, more and more tools started to emerge.
Source : Markets and Markets Most web analytics apps include multi-touch attribution reports. Then there are standalone multi-channel attribution platforms, offering extra features for conversion rate optimization, offline channel tracking, data-driven custom modelling, etc.
Most advanced solutions aren’t available out of the box. Instead, you have to install several applications, configure integrations with requested data sources, and then use the provided interfaces to code together custom data models. Such solutions are great if you have a technical marketer or a data science team. But a steep learning curve and high setup costs make them less attractive for smaller teams.
Conclusion
Multi-touch attribution modelling lifts the curtain in more steps, involved in various customer journeys. By understanding which touchpoints contribute to conversions, you can better plan your campaign types and budget allocations.
That said, to benefit from multi-touch attribution modelling, marketers also need to do the preliminary work : Determine the key goals, set up event and conversion tracking, and then — select the optimal attribution model type and tool.
Matomo combines simplicity with sophistication. We provide marketers with familiar, intuitive interfaces for setting up conversion tracking across the funnel. Then generate attribution reports, based on 100% accurate data (without any sampling or “guesstimation” applied). You can also get access to raw analytics data to create custom attribution models or plug it into another tool !
Start using accurate, easy-to-use multi-channel attribution with Matomo. Start your free 21-day trial now. No credit card requried.
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How HSBC and ING are transforming banking with AI
9 novembre 2024, par Daniel Crough — Banking and Financial Services, Featured Banking ContentWe recently partnered with FinTech Futures to produce an exciting webinar discussing how analytics leaders from two global banks are using AI to protect customers, streamline operations, and support environmental goals.
Watch the on-demand webinar : Advancing analytics maturity.
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</script>Meet the expert panel
Roshini Johri heads ESG Analytics at HSBC, where she leads AI and remote sensing applications supporting the bank’s net zero goals. Her expertise spans climate tech and financial services, with a focus on scalable analytics solutions.
Marco Li Mandri leads Advanced Analytics Strategy at ING, where he focuses on delivering high-impact solutions and strengthening analytics foundations. His background combines analytics, KYC operations, and AI strategy.
Carmen Soini Tourres works as a Web Analyst Consultant at Matomo, helping financial organisations optimise their digital presence whilst maintaining privacy compliance.Key findings from the webinar
The discussion highlighted four essential elements for advancing analytics capabilities :
1. Strong data foundations matter most
“It doesn’t matter how good the AI model is. It is garbage in, garbage out,”
Johri explained. Banks need robust data governance that works across different regulatory environments.
2. Transform rather than tweak
Li Mandri emphasised the need to reconsider entire processes :
“We try to look at the banking domain and processes and try to re-imagine how they should be done with AI.”
3. Bridge technical and business understanding
Both leaders stressed the value of analytics translators who understand both technology and business needs.
“We’re investing in this layer we call product leads,”
Li Mandri explained. These roles combine technical knowledge with business acumen – a rare but vital skill set.
4. Consider production costs early
Moving from proof-of-concept to production requires careful planning. As Johri noted :
“The scale of doing things in production is quite massive and often doesn’t get accounted for in the cost.”
This includes :
- Ongoing monitoring requirements
- Maintenance needs
- Regulatory compliance checks
- Regular model updates
Real-world applications
ING’s approach demonstrates how banks can transform their operations through thoughtful AI implementation. Li Mandri shared several areas where the bank has successfully deployed analytics solutions, each benefiting both the bank and its customers.
Customer experience enhancement
The bank’s implementation of AI-powered instant loan processing shows how analytics can transform traditional banking.
“We know AI can make loans instant for the customer, that’s great. Clicking one button and adding a loan, that really changes things,”
Li Mandri explained. This goes beyond automation – it represents a fundamental shift in how banks serve their customers.
The system analyses customer data to make rapid lending decisions while maintaining strong risk assessment standards. For customers, this means no more lengthy waiting periods or complex applications. For the bank, it means more efficient resource use and better risk management.
The bank also uses AI to personalise customer communications.
“We’re using that to make certain campaigns more personalised, having a certain tone of voice,”
noted Li Mandri. This particularly resonates with younger customers who expect relevant, personalised interactions from their bank.
Operational efficiency transformation
ING’s approach to Know Your Customer (KYC) processes shows how AI can transform resource-heavy operations.
“KYC is a big area of cost for the bank. So we see massive value there, a lot of scale,”
Li Mandri explained. The bank developed an AI-powered system that :
- Automates document verification
- Flags potential compliance issues for human review
- Maintains consistent standards across jurisdictions
- Reduces processing time while improving accuracy
This implementation required careful consideration of regulations across different markets. The bank developed monitoring systems to ensure their AI models maintain high accuracy while meeting compliance standards.
In the back office, ING uses AI to extract and process data from various documents, significantly reducing manual work. This automation lets staff focus on complex tasks requiring human judgment.
Sustainable finance initiatives
ING’s commitment to sustainable banking has driven innovative uses of AI in environmental assessment.
“We have this ambition to be a sustainable bank. If you want to be a sustainable finance customer, that requires a lot of work to understand who the company is, always comparing against its peers.”
The bank developed AI models that :
- Analyse company sustainability metrics
- Compare environmental performance against industry benchmarks
- Assess transition plans for high-emission industries
- Monitor ongoing compliance with sustainability commitments
This system helps staff evaluate the environmental impact of potential deals quickly and accurately.
“We are using AI there to help our frontline process customers to see how green that deal might be and then use that as a decision point,”
Li Mandri noted.
HSBC’s innovative approach
Under Johri’s leadership, HSBC has developed several groundbreaking uses of AI and analytics, particularly in environmental monitoring and operational efficiency. Their work shows how banks can use advanced technology to address complex global challenges while meeting regulatory requirements.
Environmental monitoring through advanced technology
HSBC uses computer vision and satellite imagery analysis to measure environmental impact with new precision.
“This is another big research area where we look at satellite images and we do what is called remote sensing, which is the study of a remote area,”
Johri explained.
The system provides several key capabilities :
- Analysis of forest coverage and deforestation rates
- Assessment of biodiversity impact in specific regions
- Monitoring of environmental changes over time
- Measurement of environmental risk in lending portfolios
“We can look at distant images of forest areas and understand how much percentage deforestation is being caused in that area, and we can then measure our biodiversity impact more accurately,”
Johri noted. This technology enables HSBC to :
- Make informed lending decisions
- Monitor environmental commitments of borrowers
- Support sustainability-linked lending programmes
- Provide accurate environmental impact reporting
Transforming document analysis
HSBC is tackling one of banking’s most time-consuming challenges : processing vast amounts of documentation.
“Can we reduce the onus of human having to go and read 200 pages of sustainability reports each time to extract answers ?”
Johri asked. Their solution combines several AI technologies to make this process more efficient while maintaining accuracy.
The bank’s approach includes :
- Natural language processing to understand complex documents
- Machine learning models to extract relevant information
- Validation systems to ensure accuracy
- Integration with existing compliance frameworks
“We’re exploring solutions to improve our reporting, but we need to do it in a safe, robust and transparent way.”
This careful balance between efficiency and accuracy exemplifies HSBC’s approach to AI.
Building future-ready analytics capabilities
Both banks emphasise that successful analytics requires a comprehensive, long-term approach. Their experiences highlight several critical considerations for financial institutions looking to advance their analytics capabilities.
Developing clear governance frameworks
“Understanding your AI risk appetite is crucial because banking is a highly regulated environment,”
Johri emphasised. Banks need to establish governance structures that :
- Define acceptable uses for AI
- Establish monitoring and control mechanisms
- Ensure compliance with evolving regulations
- Maintain transparency in AI decision-making
Creating solutions that scale
Li Mandri stressed the importance of building systems that grow with the organisation :
“When you try to prototype a model, you have to take care about the data safety, ethical consideration, you have to identify a way to monitor that model. You need model standard governance.”
Successful scaling requires :
- Standard approaches to model development
- Clear evaluation frameworks
- Simple processes for model updates
- Strong monitoring systems
- Regular performance reviews
Investing in people and skills
Both leaders highlighted how important skilled people are to analytics success.
“Having a good hiring strategy as well as creating that data literacy is really important,”
Johri noted. Banks need to :
- Develop comprehensive training programmes
- Create clear career paths for analytics professionals
- Foster collaboration between technical and business teams
- Build internal expertise in emerging technologies
Planning for the future
Looking ahead, both banks are preparing for increased regulation and growing demands for transparency. Key focus areas include :
- Adapting to new privacy regulations
- Making AI decisions more explainable
- Improving data quality and governance
- Strengthening cybersecurity measures
Practical steps for financial institutions
The experiences shared by HSBC and ING provide valuable insights for financial institutions at any stage of their analytics journey. Their successes and challenges outline a clear path forward.
Key steps for success
Financial institutions looking to enhance their analytics capabilities should :
- Start with strong foundations
- Invest in clear data governance frameworks
- Set data quality standards
- Build thorough documentation processes
- Create transparent data tracking
- Think strategically about AI implementation
- Focus on transformative rather than small changes
- Consider the full costs of AI projects
- Build solutions that can grow
- Balance innovation with risk management
- Invest in people and processes
- Develop internal analytics expertise
- Create clear paths for career growth
- Foster collaboration between technical and business teams
- Build a culture of data literacy
- Plan for scale
- Establish monitoring systems
- Create governance frameworks
- Develop standard approaches to model development
- Stay flexible for future regulatory changes
Learn more
Want to hear more insights from these industry leaders ? Watch the complete webinar recording on demand. You’ll learn :
- Detailed technical insights from both banks
- Extended Q&A with the speakers
- Additional case studies and examples
- Practical implementation advice
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Watch the on-demand webinar : Advancing analytics maturity.
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